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Article

Applicability Evaluation of Antarctic Ozone Reanalysis and Merged Satellite Datasets

by
Junzhe Chen
1,2,
Yu Zhang
1,2,3,*,
Houxiang Shi
1,4,
Hao Hu
1,2 and
Jianjun Xu
2,4
1
Laboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
2
Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China
3
Key Laboratory of Space Ocean Remote Sensing and Application of Ministry of Natural Resources/Key Laboratory of Climate Resources and Environment in Continental Shelf Sea and Deep Ocean, Zhanjiang 524088, China
4
CMA-GDOU Joint Laboratory for Marine Meteorology, South China Sea Institute of Marine Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(6), 696; https://doi.org/10.3390/atmos16060696
Submission received: 30 April 2025 / Revised: 28 May 2025 / Accepted: 6 June 2025 / Published: 10 June 2025
(This article belongs to the Section Climatology)
Browse Figures
Versions Notes

Abstract

In this study, based on total column ozone observations from eight Antarctic stations, we evaluate the applicability of ERA5, C3S-MSR, MERRA-2, and JRA-55 reanalysis datasets and the NIWA-BS merged satellite dataset, in terms of interannual variation and long-term trend, using the correlation coefficient (R), root-mean-square error (RMSE), interannual variability skill score (IVS), and linear trend bias (TrBias). The results show that for interannual variation, C3S-MSR performs well at multiple stations, while JRA-55 performs poorly at most stations, especially Marambio, Rothera, and Faraday/Vernadsky, which are located at lower latitudes on the Antarctic Peninsula. Additionally, all datasets show significantly higher RMSE at Dumont D’Urville and Arrival Heights, which generally are located around the edge of the Antarctic stratospheric vortex where total column ozone values are more variable and on average larger than in the core of the vortex. The comprehensive ranking results show that C3S-MSR performs the best, followed by ERA5 and NIWA-BS, with MERRA-2 and JRA-55 ranking lower. For the long-term trend, each of the datasets has large bias values at Arrival Heights, and the absolute TrBias values of JRA-55 are larger at three stations on the Antarctic Peninsula. The overall averaged results show that C3S-MSR and NIWA-BS have the smallest absolute TrBias, and perform best in reflecting the Antarctic ozone trends, while ERA5 and JRA-55 significantly overestimate the Antarctic ozone recovery trend and perform poorly. Based on our analysis, the C3S-MSR dataset can be recommended to be prioritized when analyzing the interannual variations in Antarctic stratospheric ozone, and both the C3S-MSR reanalysis and NIWA-BS datasets should be prioritized for trend analysis.

1. Introduction

Since the late 1970s, stratosphere total ozone column amounts over Antarctic have declined significantly, leading to the formation of the Antarctic ozone hole during Austral spring (September–November) [1,2,3]. Continued depletion of the ozone layer causes excess solar ultraviolet radiation to penetrate the atmosphere, posing a serious threat to human health and Antarctic ecosystems [4]. At the same time, the ozone layer absorbs ultraviolet radiation and serves as the primary heat source in the stratosphere. Changes in its concentration have significantly altered the atmospheric circulation in the Southern Hemisphere, exerting far-reaching impacts on weather and climate both regionally and globally [5,6,7,8,9]. International efforts and treaties have resulted in phasing out emissions of many ozone-depleting substances, whose atmospheric concentrations have peaked around the year 2000 and have been steadily declining ever since. The first signs of the associated recovery of the ozone layer have been detected in the Antarctic ozone hole. Therefore, it is of great scientific significance to monitor the long-term variations and trends of Antarctic stratospheric ozone.
Observational data from Antarctic stations are crucial for analyzing long-term ozone variations. Nevertheless, the scarcity of stations, low spatial coverage, and poor temporal continuity in the Antarctic region limit research efforts to assess ozone layer health and the state of the ozone hole, and for recovery detection. Reanalysis data and merged satellite products can make up for these shortcomings; these include a series of global atmospheric reanalysis products like MERRA-2, ERA5, JRA-55, and C3S-MSR [10,11,12,13], as well as satellite-based merged products like NIWA-BS [14]. Reanalysis systems assimilate multi-source observations and generate gridded products with high spatial coverage and temporal continuity through model outputs [15,16,17,18,19,20,21,22,23], offering substantial advantages for Antarctic ozone research. These datasets have been used to investigate the characterization of ozone variations and ozone depletion mechanisms [14,24,25]. For example, future variations in total column ozone and its driving factors have been analyzed using statistical regression methods based on the C3S-MSR multi-sensor reanalysis dataset [13]. Similarly, the zonal asymmetry of Antarctic ozone has been explored using the MERRA-2 reanalysis dataset [15].
Although reanalysis and merged satellite datasets have addressed the issue of spatial and temporal continuity in previous data, as fusion products of observations and numerical models, they cannot fully reflect the true state of the atmosphere [26,27,28]. Additionally, some effort has been put into evaluating the applicability of these datasets. For example, the applicability of the MERRA-2 ozone reanalysis product was assessed using satellite and ozone sounding data [29]; the OMPS Limb Profiler (Ozone Mapping and Profiler Suite-LP version 2.5) satellite dataset was assessed based on ozonesonde data from eight Antarctic stations [30]; and the applicability of NOAA 20 CrIS and AIRS_V7 satellite data, as well as ERA5 data, was evaluated in polar regions using total column ozone and ozone partial pressure observations from ground-based stations, along with the root-mean-square error (RMSE) and correlation coefficients [31]. However, due to differences in evaluation content and methodologies across studies, the final assessment results exhibit certain discrepancies, and to our knowledge there are few assessments of the interannual variations and long-term trends of Antarctic ozone. Hence, it is necessary to focus on these two aspects and conduct a systematic evaluation of reanalysis and merged satellite products to assess their uncertainty.
In this study, based on the total column ozone observations from eight Antarctic ground stations, we evaluated five gridded datasets—the ERA5, MERRA-2, JRA-55, and C3S-MSR reanalysis datasets, as well as the NIWA-BS merged satellite dataset—using the correlation coefficient (R), the root-mean-square error (RMSE), and interannual variability skill score (IVS), in order to analyze the applicability of these datasets in studying the long-term variations and trends of Antarctic ozone.

2. Data and Methodology

2.1. Introduction to Data

In this study, total column ozone (TCO, unit: DU) observations from eight Antarctic stations, together with four sets of reanalysis datasets (ERA5, MERRA2, JRA-55, and C3S-MSR), and one merged satellite dataset (NIWA-BS) were used. Since continuous satellite records for the Southern Hemisphere have only been available since 1979 [32], we only used reanalysis and merged satellite datasets from 1980 onward. Note that due to limitations in the continuity of Antarctic station observations, the time periods of used data for each station vary.

2.1.1. Antarctic Station Observations

The Antarctic station observations selected for this study were obtained from the World Ozone and UV Radiation Data Center (WOUDC) and the British Antarctic Survey (BAS). The daily total column ozone data at Antarctic ground-based stations are mainly measured using Dobson ozone spectrophotometers (Dobson, mostly manufactured by R&J Beck, London, UK), Brewer ozone spectrophotometers (Brewer, manufactured by Kipp & Zonen, Delft, Netherlands), and the Système d’Analyse par Observation Zénithale (SAOZ) UV–Visible spectrometer (manufactured by LATMOS, Guyancourt, France). Regarding data coverage, most stations have a coverage of more than 75%, indicating that for each month, observations exist on more than 75% of the days, while Arrival Heights only has coverage of around 50%. In this study, we averaged the daily observations into monthly mean values for further analysis.
The details of the station observations are shown in Table 1, and the locations of the stations are shown in Figure 1.

2.1.2. Reanalysis and Merged Satellite Datasets

The ozone reanalysis and merged satellite datasets used in this study were obtained from the European Centre for Medium-Range Weather Forecasts fifth-generation atmospheric reanalysis dataset (ECMWF Reanalysis v5, ERA5); the Bodeker Scientific filled (BS–filled) Multi-Satellite V3.4.1 dataset of New Zealand’s National Institute of Water and Atmospheric Research (NIWA-BS); the Copernicus Climate Change Service-Multi Sensor Reanalysis dataset (C3S-MSR); the global atmospheric reanalysis dataset developed by the Japan Meteorological Agency (Japanese 55-year Reanalysis, JRA-55); and the Global Atmospheric Reanalysis dataset (Modern-Era Retrospective Analysis for Research and Applications, Version 2, MERRA-2) developed by the National Aeronautics and Space Administration of the United States.
All five datasets evaluated in this study contain monthly data. There are some differences in resolution, time periods, and data assimilation techniques among these datasets. Their temporal coverage and spatial resolutions are listed in Table 2, where the spatial resolution of C3S-MSR is 1° × 1° for the period 1970–1979 and 0.5° × 0.5° after 1979.

2.2. Introduction to the Methodology

In this study the following statistical metrics are used:
R = ∑ i = 1 n m i − m ¯ o i − o ¯ ∑ i = 1 n m i − m ¯ 2 ∑ i = 1 n o i − o ¯ 2
R M S E = 1 n ∑ i = 1 n m i − o i 2
I V S = σ m σ o − σ o σ m 2
T r B i a s = T r m − T r o
In Equations (1)–(4), m denotes the five datasets to be evaluated, o denotes the station observations, σ is the standard deviation, and Tr is the linear trend. Table 3 provides the details of these metrics. The closer the values of R, RMSE, IVS, and TrBias to their optimal values, the smaller the discrepancies in error, interannual variability, and trend between the evaluated dataset and the observations.
Evaluation methods and steps: 1. Calculate the correlation coefficient (R), root-mean-square error (RMSE), interannual variability skill score (IVS), and trend bias (TrBias) between eight station observations and time series of five gridded datasets at the corresponding latitude and longitude. Then, calculate the arithmetic mean of these four metrics at eight stations. 2. Based on the arithmetic mean of R, RMSE, and IVS at eight stations, each of the three metrics is ranked accordingly. The average of rankings for these metrics is then used to determine a comprehensive ranking of each dataset in terms of interannual variation. 3. For the long-term trend, the ranking results of five datasets are given directly based on the overall average of TrBias at eight stations. Among the above evaluation metrics, the correlation coefficient (R) is ranked in descending order, whereas RMSE, IVS, and TrBias are ranked in ascending order.

3. Results

3.1. Applicability Evaluation of Interannual Variation

Antarctic stratospheric ozone depletion is most severe during Austral spring, namely from September to November, so this study will focus its evaluation on September, October, November, and the Spring Average.

3.1.1. Assessment of Correlation Coefficients

Figure 2a–d show the correlation coefficients of the interannual variations in total column ozone between the eight Antarctic stations and the corresponding locations in the five datasets for September, October, November, and the Spring Average, respectively. All correlation coefficients exceed the 2σ significance level. The larger the correlation coefficients of a dataset at the eight stations, the better the performance, as reflected by the larger area enclosed by the line segments. In September, the correlation coefficients of the C3S-MSR dataset are larger than 0.9 at most stations, the area enclosed by the line segments is the largest, and the overall performance is the best; the performance of the ERA5 dataset is close to that of C3S-MSR; the NIWA-BS dataset is slightly inferior to the previous two datasets; and the performances of JRA-55 and MERRA-2 are poorer, with the correlation coefficients of MERRA-2 being the smallest at several stations, and enclosing the smallest area, reflecting the worst overall performance. Among the eight stations, all datasets show the highest correlation coefficients at Dumont D’Urville, while the smallest correlation coefficients are observed at Marambio and Arrival Heights. Among them, the correlation coefficients of JRA-55 and MERRA-2 are smaller at the Rothera and Faraday/Vernadsky stations than those of the remaining three datasets.
In October and November, the correlation coefficients of C3S-MSR, ERA5, NIWA-BS, and MERRA-2 exceed 0.9 at most stations, with a larger area enclosed by the line segments and a better overall performance; and the correlation coefficients of JRA-55 are smaller at most stations, with the smallest enclosed area, and the worst overall performance. Among the eight stations, the correlation coefficients of JRA-55 at Faraday/Vernadsky, Rothera, and Marambio are significantly smaller, even less than 0.4, while the other datasets only exhibit slightly lower correlation coefficients at Arrival Heights. In addition, compared to October, the correlation coefficients of C3S-MSR, ERA5, NIWA-BS, and MERRA-2 at Marambio and Arrival Heights increase in November.
In terms of the Spring Average, C3S-MSR, ERA5, NIWA-BS, and MERRA-2 datasets have larger correlation coefficients, enclose a larger area, and perform better overall at most stations; in contrast, JRA-55 performs the worst. Among the eight stations, JRA-55 has the smallest correlation coefficients at Marambio, Rothera, and Faraday/Vernadsky due to the impact of October and November. Additionally, owing to the influence of September, the rest of the datasets only show correlation coefficients slightly below 0.9 at Marambio and Arrival Heights.
In general, C3S-MSR performs the best, followed by ERA5, NIWA-BS, and MERRA-2 in descending order. JRA-55 has the worst overall performance, with the smallest correlation coefficients observed at Marambio, Rothera, and Faraday/Vernadsky, suggesting that it fails to represent the Antarctic ozone variations in the region around 60°W. Previous studies have found a close relationship between the Antarctic polar vortex and ozone depletion [33,34,35]. The low-temperature environment inside the polar vortex provides favorable conditions for catalytic photochemical ozone destruction, while the strong circumpolar westerlies at the vortex edge greatly hinder the meridional transport of ozone, resulting in a significant difference in total column ozone between the inside and outside of the vortex. Consequently, stations at the edge of the polar vortex will be significantly affected by the morphology and position of the polar vortex, and even slight shifts of the vortex can lead to large changes in the total column ozone at these stations.
Previous studies have pointed out that between 1980 and 2009, the center of the Antarctic polar vortex gradually shifted eastward, and its boundary contracted poleward over the West Antarctic region; the three stations of Marambio, Rothera, and Faraday/Vernadsky, which are located at lower latitudes on the Antarctic Peninsula near 60°W, are more likely to be close to the edge of the polar vortex [36], making the total column ozone at these stations more sensitive to the position of the vortex. If a numerical model has biases in simulating the polar vortex morphology, the output ozone reanalysis dataset will exhibit large errors, which may explain why JRA-55 fails to accurately reflect the Antarctic ozone variations at the three stations near 60° W. In addition, the lower spatial resolution of JRA-55 compared to the other datasets may smooth out variations in TCO values caused by changes in the position of polar vortex, which may also contribute to its poor performance.

3.1.2. Assessment of Root-Mean-Square Errors

Similar to the correlation coefficient in Figure 2, Figure 3 illustrates the results of the RMSE evaluation. Unlike the correlation coefficients, smaller RMSE values indicate better performance, which is shown in the figure by a smaller area enclosed by the line segments. As shown in Figure 3a, in September, the root-mean-square errors of C3S-MSR dataset are less than 15 DU at most stations, the area enclosed by the line segments is the smallest, and the overall performance is the best; the performance of ERA5 is slightly inferior to that of C3S-MSR, followed by the NIWA-BS dataset, while the performances of JRA-55 and the MERRA-2 are poorer. The RMSE values of MERRA-2 dataset are greater than 20 DU at several stations, the area enclosed by the line segments is the largest, and the overall performance is the worst. Comparing the eight stations, the root-mean-square errors are large for all the datasets at Marambio and Arrival Heights; at Halley station, the RMSE value of MERRA-2 is remarkably large, while the other data have smaller errors, showing significant differences in RMSE values among the datasets at this station.
From October to November, compared with September, the root-mean-square errors of ERA5, MERRA2, C3S-MSR, and NIWA-BS generally decrease, and the enclosed area becomes smaller and the overall performance is better; however, the root-mean-square errors of all the datasets are slightly larger at Dumont D’Urville and Arrival Heights. JRA-55, by contrast, behaves differently from the other datasets, with generally larger RMSE at several stations and the worst overall performance, especially at Marambio, Rothera and Faraday/Vernadsky on the Antarctic Peninsula, where the RMSE values are significantly higher than that of the other datasets.
For the Spring Average results, the performance is significantly affected by the months with larger or smaller errors from September to November. Similar to the results for October and November, all datasets show relatively large root-mean-square errors at Dumont D’Urville and Arrival Heights, besides the three stations located on the Antarctic Peninsula. Antarctic stratospheric ozone has a zonally asymmetric distribution, with Dumont D’Urville and Arrival Heights located close to the edge of the Antarctic stratospheric vortex where total column ozone is more variable and on average larger than in the vortex core [37]. As a result, the baseline total column ozone at these two stations is relatively high, so the RMSE values are likely to be correspondingly large.
In summary, the overall error of C3S-MSR is small, followed by that of ERA5 and NIWA-BS, and finally MERRA-2 and JRA-55, while JRA-55 has the largest overall error. The RMSE evaluation results are in general agreement with those of the correlation coefficients.

3.1.3. Assessment of Interannual Variability Skill Scores

Figure 4 shows the evaluation results of the IVS, which characterizes interannual variability. As shown in the figure, there are significant differences in the interannual variability skill scores among different stations and datasets. Specifically, in September, the C3S-MSR dataset shows smaller IVS values at most stations, encloses the smallest area, and performs the best overall. ERA5 ranks second only to C3S-MSR, followed by NIWA-BS, while MERRA-2 and JRA-55 perform the worst. Among the eight stations, the interannual variability skill score (IVS) values for all datasets are significantly larger at Rothera and notably smaller at Syowa.
In October, C3S-MSR shows the smallest interannual variability skill scores and the smallest area enclosed by line segments at most stations, and performs the best overall. ERA5, NIWA-BS, and MERRA-2 perform slightly less well than C3S-MSR, while JRA-55 performs the worst. Among the eight stations, all datasets show relatively small IVS values at Syowa, while larger values are observed at ZhongShan Station.
In November, the performance of each dataset has obvious changes compared to September and October: ERA5 exhibits the smallest interannual variability skill scores at most stations, enclosing the smallest area and thus performing the best overall, followed by C3S-MSR, then MERRA-2 and JRA-55, while NIWA-BS performs the worst. Among the eight stations, the IVS values vary considerably across different datasets.
For the Spring Average, all datasets show relatively consistent performance across the stations, with the smallest interannual variability skill scores observed at Halley, Syowa, and Dumont D’Urville.
In contrast to the evaluation results of correlation coefficients and root-mean-square errors, for all datasets, the interannual variability skill scores show large discrepancies among stations, indicating that all datasets perform poorly in capturing the interannual variability of total column ozone. In general, C3S-MSR performs the best, although it shows large fluctuations across different stations and months; ERA5 ranks second; followed by NIWA-BS and MERRA-2; and JRA-55 performs the worst.

3.1.4. Comprehensive Interannual Variation Assessment

In order to comprehensively evaluate the applicability of the five datasets for the interannual variation of Antarctic ozone, we combined the three metrics of the correlation coefficient (R), the root-mean-square error (RMSE), and interannual variability skill score (IVS) for each dataset and eight Antarctic station observations in four time periods (September, October, November, and Spring Average) to obtain a comprehensive ranking of each dataset.
Figure 5 shows the results of the three metrics (R, RMSE, and IVS) after averaging across eight stations, thereby reflecting the overall performance at all stations. In September, C3S-MSR shows the largest correlation coefficient, the smallest root-mean-square error, and the smallest interannual variability skill score, demonstrating the best overall performance; MERRA-2 exhibits the smallest correlation coefficient and the largest RMSE, and JRA-55 has the largest IVS. In October, C3S-MSR remains the best performer in terms of all three metrics, while JRA-55 performs the worst. In November, C3S-MSR again has the largest correlation coefficient and the smallest RMSE, whereas ERA5 has the smallest interannual variability skill score; JRA-55 shows the smallest correlation coefficient and the largest RMSE, while NIWA-BS exhibits the largest IVS. For the Spring Average, C3S-MSR performs the best overall, while JRA-55 performs the worst. The comprehensive evaluation results are generally consistent with the evaluation results at individual stations. Furthermore, in October, the top four datasets in the three metrics show relatively consistent performance; in November, the top four datasets in terms of correlation coefficient and RMSE also exhibit consistent performance.
By calculating the arithmetic mean of the ranks for three metrics (R, RMSE, and IVS), average rankings of the five datasets are derived. These rankings, covering four time periods and the composite, are shown in Table 4. C3S-MSR consistently performs the best across all four periods, always ranks first, followed closely by ERA5, which ranks second overall. NIWA-BS generally ranks in the middle across the different periods, although its performance in November is relatively poor. MERRA-2 performs relatively well in November but shows larger deviations overall. JRA-55 consistently ranks last across the four periods, demonstrating the poorest overall performance. C3S-MSR performs optimally across all periods and can describe the variations in Antarctic total column ozone more accurately, followed by ERA5 and NIWA-BS. MERRA-2 performs erratically, whereas JRA-55 shows smaller correlation coefficients, larger RMSE values, and greater differences in IVS values, failing to adequately represent the interannual variation of Antarctic ozone.

3.2. Evaluation of Long-Term Trends in the Recovery Period

Around the year 2000, the equivalent effective stratospheric chlorine (EESC), the main chemical driver of Antarctic ozone depletion, reached its peak, and the total column ozone dropped to a historical minimum [38,39,40]. After the year 2000, Antarctic stratospheric ozone has gradually begun to recover. Therefore, this study selects the post-2000 ozone recovery period [3,40] as the focus and discusses the ability of different datasets to describe the linear recovery trends during this period.

3.2.1. Long-Term Trends Assessment at Individual Stations

Figure 6 shows the absolute values of linear trend bias (units: DU/year) between the five datasets and observations during the Antarctic ozone recovery period (2000–2019). For each station, we calculate a 1σ uncertainty interval for its trend. A trend falling outside this interval indicates a statistically meaningful bias between the dataset and the observation. As shown in the figure, in September, C3S-MSR exhibits smaller absolute values of trend bias (TrBias) at most stations, with the smallest area enclosed by the line segments, demonstrating the best capability in capturing the trend. ERA5, NIWA-BS, and JRA-55 show similar performance, while MERRA-2 has the largest absolute TrBias values at many stations, leading to the largest enclosed area and the poorest performance in describing the recovery trend. Among the eight stations, all datasets show relatively large absolute TrBias at Arrival Heights, which is also confirmed by statistical testing. In October, the absolute TrBias values at most stations are evidently smaller compared to September, with smaller differences among the datasets and more consistent performance across stations. Similar to September, all datasets still exhibit the largest absolute TrBias at Arrival Heights.
In November, C3S-MSR, ERA5, NIWA-BS, and MERRA-2 show relatively consistent performance. In contrast, JRA-55 exhibits larger absolute TrBias values at multiple stations. Unlike the other datasets, JRA-55 shows abnormally large absolute TrBias values at the three stations located on the Antarctic Peninsula, namely Marambio, Rothera, and Faraday/Vernadsky, with the trends falling outside the intervals. This finding is consistent with the results from the interannual variation assessment. For the Spring Average, most datasets display relatively consistent absolute TrBias values, while JRA-55 performs poorly overall, showing large absolute TrBias values at Arrival Heights, Marambio, Rothera, and Faraday/Vernadsky.
In general, C3S-MSR, NIWA-BS, and ERA5 datasets have smaller absolute TrBias, while the absolute TrBias of the MERRA-2 dataset fluctuates more, and JRA-55 has the largest absolute TrBias.

3.2.2. Average Trends Assessment at Multiple Stations

The trend biases of each dataset at eight stations are averaged to represent the overall trend bias for the Antarctic region, which serves as an indicator of each dataset’s ability to describe the overall Antarctic ozone trends. Based on the trends of station observations, we calculate 1σ uncertainty intervals. As shown in Figure 7, in September, the absolute trend bias (TrBias) of NIWA-BS is the smallest, with its trend only 1.49 DU/dec lower than the observation, followed by C3S-MSR; the trends of ERA5 and JRA-55 are significantly higher than the observation; and the absolute TrBias of MERRA-2 is the largest, with its trend 7.95 DU/dec lower than the observation.
In October, MERRA-2 has the smallest absolute TrBias, with its trend 0.96 DU/dec lower than the observation, followed by C3S-MSR. The trend of NIWA-BS is lower than the observation, while ERA5 and JRA-55 are higher than the observation; ERA5 has the largest absolute TrBias, with its trend 6.36 DU/dec higher than the observation. In November, both NIWA-BS and C3S-MSR have an absolute TrBias of 0.88 DU/dec, but the former has a positive bias and the latter has a negative one; the trends of MERRA-2, ERA5, and JRA-55 are all higher than the observation, with JRA-55 showing the largest absolute TrBias, as its trend surpasses the uncertainty interval and is 15.95 DU/dec higher than the observation.
For the Spring Average, C3S-MSR has the smallest absolute TrBias, with a trend only 0.01 DU/dec lower than the observation; the trends of NIWA-BS and MERRA-2 are also lower than the observation. Consistent with the findings of September, October, and November, the trends for ERA5 and JRA-55 are higher than the observation. Among them, JRA-55 has the largest absolute TrBias, with its trend 8.33 DU/dec higher than the observation.
In general, C3S-MSR and NIWA-BS perform well, while the trends of ERA5 and JRA-55 are all higher than observations, which overestimate the Antarctic ozone recovery trend, and MERRA-2 shows more inconsistent performance. Furthermore, JRA-55 displays a larger deviation in November, which surpasses the uncertainty range and needs attention.
Based on the trend bias (TrBias) results above, the datasets are ranked in ascending order according to the absolute value of their bias. The rankings across the four periods are then averaged to obtain the overall average ranking, as shown in Table 5. C3S-MSR and NIWA-BS rank highest, indicating that they can more accurately reflect the variation trends of total column ozone over Antarctic during the recovery period. MERRA-2 shows moderate performance, while JRA-55 and ERA5 exhibit the largest bias in describing the trend.

4. Discussion and Conclusions

Reanalysis and merged satellite products can effectively compensate for the deficiencies in spatial and temporal resolution of Antarctic station observations. Nevertheless, their applicability varies among different products. In this study, based on the total column ozone observations from eight Antarctic stations, we evaluated five widely used gridded ozone datasets, leading to the following main conclusions:
  • In terms of interannual variation, for the correlation coefficients (R), C3S-MSR performed well at all eight stations and showed the best overall performance, while JRA-55 performed poorly at most stations, especially Marambio, Rothera, and Faraday/Vernadsky, which are located at lower latitudes on the Antarctic Peninsula near 60°W. For the root-mean-square error (RMSE), C3S-MSR was the best and JRA-55 was the worst. All datasets exhibited larger errors at Dumont D’Urville and Arrival Heights, which are located close to the edge of the Antarctic stratospheric vortex where total column ozone is on average larger than in the vortex core. Regarding the interannual variability skill score (IVS), C3S-MSR showed relatively small differences, but exhibited some fluctuations among different stations and months. JRA-55 showed the largest overall differences, especially for the above-mentioned three stations on the Antarctic Peninsula and the two stations in the ozone high value area.
  • Combining the three evaluation metrics and the average results of eight stations, this study obtained the overall rankings of the datasets for interannual variation. The ranking results showed that the C3S-MSR reanalysis dataset performed best overall, followed by the ERA5 reanalysis dataset and the NIWA-BS merged satellite dataset. The MERRA-2 and JRA-55 reanalysis datasets ranked lowest. Among them, JRA-55 had the worst performance and failed to present the interannual variation of Antarctic ozone.
  • The evaluation of trends during the recovery period (2000–2019) showed that, for most time periods, the absolute trend bias (TrBias) values of all datasets were within an acceptable range for most stations, and were able to reflect the Antarctic ozone trends. However, large differences were observed at some stations, especially Arrival Heights. The absolute TrBias of JRA-55 was larger, especially for the three stations on the Antarctic Peninsula, namely Marambio, Rothera, and Faraday/Vernadsky. Regarding the overall trend of eight stations, the absolute TrBias values of C3S-MSR and NIWA-BS were smaller, accurately reflecting the ozone trend in the Antarctic area; the performance of MERRA-2 was not stable, with large differences from month to month; whereas ERA5 and JRA-55, on the other hand, significantly overestimated the recovery trend of Antarctic ozone and performed poorly. In particular, JRA-55 showed considerable deviations in certain months, suggesting that its use in analyzing Antarctic ozone trends requires careful attention.
The MSR (multi-sensor reanalysis) dataset provided by C3S (Copernicus Climate Change) incorporates the factors of solar zenith angle (SZA), viewing zenith angle (VZA), and effective ozone temperature and applies multiple regression to correct multiple satellite data. Additionally, the corrected satellite data were assimilated into the global chemistry transport model [13,41,42], which may explain why C3S-MSR performed best in the evaluation. However, the C3S-MSR dataset has certain defects in terms of data validity period, temporal continuity, and spatial resolution in Antarctica. Hence, when conducting the study of interannual variation, it can be analyzed together with other datasets (such as ERA5). Both the C3S-MSR reanalysis dataset, which assimilates satellite data, and the NIWA-BS merged satellite dataset are more advantageous in trend analysis, and can be given priority for use when analyzing the Antarctic ozone trends.
In this study, most stations have some missing observations in certain months, which may lead to errors between the observed monthly means and the true monthly means. This issue is particularly significant for Arrival Heights, which has a coverage of only around 50%. Therefore, future studies could further investigate how well the monthly mean station observations represent the true monthly values. In addition, this study only evaluated the bias of each dataset, without exploring the underlying causes in depth. In follow-up studies, the relationship between the polar vortex morphology and ozone could be explored from the perspective of the polar vortex morphology and position demonstrated by the reanalysis dataset, so as to carry out the study of the bias attribution.

Author Contributions

J.C.: Conceptualization, data curation, software, visualization, writing—original draft, writing—review and editing. Y.Z.: Conceptualization, supervision, writing—review and editing. H.S.: Software and visualization. H.H.: Writing—review and editing. J.X.: Conceptualization and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42205076, 72293604), the Guangdong Basic and Applied Basic Research Foundation (NO. 2024A1515010064) and the First-Class Discipline Plan of Guangdong Province (080503032101, 231420003).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data for this study are publicly available. ERA5 reanalysis dataset is available from https://doi.org/10.24381/cds.adbb2d47 (Accessed on 4 June 2024); C3S-MSR reanalysis dataset is available from https://doi.org/10.24381/cds.4ebfe4eb (Accessed on 4 June 2024); JRA-55 reanalysis dataset is available from https://doi.org/10.5065/D60G3H5B (Accessed on 4 June 2024); BS Filled Total Column Ozone Database V3.4.1 (3.4.1) NIWA-BS merged satellite dataset is available from https://zenodo.org/records/7447757 (Accessed on 4 June 2024); MERRA-2 reanalysis dataset is available from https://doi.org/10.5067/5KFZ6GXRHZKN (Accessed on 4 June 2024); Ozone observation data from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and British Antarctic Survey (BAS) are available from https://doi.org/10.14287/10000004 and https://www.bas.ac.uk (Accessed on 30 January 2024).

Acknowledgments

We acknowledge the support of the Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters (GSTOEW). The Antarctic station ozone data and 5 grid datasets (ERA5, NIWA-BS, C3S-MSR, JRA-55 and MERRA-2) used in this article are all publicly available for download, and we would like to express our gratitude to all providers.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Farman, J.C.; Gardiner, B.G.; Shanklin, J.D. Large losses of total ozone in Antarctica reveal seasonal ClO/NOx interaction. Nature 1985, 315, 207–210. [Google Scholar] [CrossRef]
  2. Solomon, S. The discovery of the Antarctic ozone hole. Nature 2019, 575, 46–48. [Google Scholar] [CrossRef]
  3. Kuttippurath, J.; Lefèvre, F.; Pommereau, J.; Roscoe, H.K.; Goutail, F.; Pazmiño, A.; Shanklin, J.D. Antarctic ozone loss in 1979–2010: First sign of ozone recovery. Atmos. Chem. Phys. 2013, 13, 1625–1635. [Google Scholar] [CrossRef]
  4. Alvim-Ferraz, M.C.M.; Sousa, S.I.V.; Martins, F.G.; Ferraz, M.P. Tropospheric and Stratospheric Ozone: Scientific History and Shifts in Early Perspectives Regarding the Impact on Human Health. Atmosphere 2024, 15, 1504. [Google Scholar] [CrossRef]
  5. Son, S.W.; Gerber, E.P.; Perlwitz, J.; Polvani, L.M.; Gillett, N.P.; Seo, K.H.; Eyring, V.; Shepherd, T.G.; Waugh, D.; Akiyoshi, H.; et al. Impact of stratospheric ozone on Southern Hemisphere circulation change: A multimodel assessment. J. Geophys. Res. 2010, 115, D00M07. [Google Scholar] [CrossRef]
  6. Zambri, B.; Solomon, S.; Thompson, D.W.; Fu, Q. Emergence of southern hemisphere stratospheric circulation changes in response to ozone recovery. Nat. Geosci. 2021, 14, 638–644. [Google Scholar] [CrossRef]
  7. Polvani, L.M.; Waugh, D.W.; Correa, G.J.; Son, S.W. Stratospheric ozone depletion: The main driver of twentieth-century atmospheric circulation changes in the southern hemisphere. J. Clim. 2011, 24, 795–812. [Google Scholar] [CrossRef]
  8. Ferreira, D.; Marshall, J.; Bitz, C.M.; Solomon, S.; Plumb, A. Antarctic Ocean and sea ice response to ozone depletion: A two-time-scale problem. J. Clim. 2015, 28, 1206–1226. [Google Scholar] [CrossRef]
  9. Turner, J.; Comiso, J.; Marshall, G.; Lachlan-Cope, T.A.; Bracegirdle, T.; Maksym, T.; Meredith, M.P.; Wang, Z.; Orr, A. Non-annular atmospheric circulation change induced by stratospheric ozone depletion and its role in the recent increase of Antarctic sea ice extent. Geophys. Res. Lett. 2009, 36, L08502. [Google Scholar] [CrossRef]
  10. Rienecker, M.; Suarez, M.J.; Gelaro, R.; Todling, R.; Bacmeister, J.; Liu, E.; Bosilovich, M.G.; Schubert, S.D.; Takacs, L.; Kim, G.-K.; et al. MERRA: NASA’s modern-ERA retrospective analysis for research and applications. J. Clim. 2011, 24, 3624–3648. [Google Scholar] [CrossRef]
  11. Kobayashi, S.; Ota, Y.; Harada, Y.; Ebita, A.; Moriya, M.; Onoda, H.; Onogi, K.; Kamahori, H.; Kobayashi, C.; Endo, H.; et al. The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteorol. Soc. Jpn. 2015, 93, 5–48. [Google Scholar] [CrossRef]
  12. Soci, C.; Hersbach, H.; Simmons, A.; Poli, P.; Bell, B.; Berrisford, P.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Radu, R.; et al. The ERA5 global reanalysis from 1940 to 2022. Q. J. R. Meteorol. Soc. 2024, 150, 4014–4048. [Google Scholar] [CrossRef]
  13. Li, Y.J.; Chipperfield, M.P.; Feng, W.H.; Dhomse, S.S.; Pope, R.J.; Li, F.Q.; Guo, D. Analysis and attribution of total column ozone changes over the Tibetan Plateau during 1979–2017. Atmos. Chem. Phys. 2020, 20, 8627–8639. [Google Scholar] [CrossRef]
  14. Bodeker, G.E.; Kremser, S. Indicators of Antarctic ozone depletion: 1979 to 2019. Atmos. Chem. Phys. 2020, 21, 5289–5300. [Google Scholar] [CrossRef]
  15. Ivaniha, O. Long-term analysis of the Antarctic total ozone zonal asymmetry by MERRA-2 and CMIP6 data. Ukr. Antarct. J. 2020, 1, 41–55. [Google Scholar] [CrossRef]
  16. Li, Y.J.; Dhomse, S.S.; Chipperfield, M.P.; Feng, W.H.; Chrysanthou, A.; Xia, Y.; Guo, D. Effects of reanalysis forcing fields on ozone trends and age of air from a chemical transport model. Atmos. Chem. Phys. 2022, 22, 10635–10656. [Google Scholar] [CrossRef]
  17. Abalos, M.; Polvani, L.; Calvo, N.; Kinnison, D.; Ploeger, F.; Randel, W.; Solomon, S. New insights on the impact of ozone-depleting substances on the Brewer-Dobson circulation. J. Geophys. Res. Atmos. 2019, 124, 2435–2451. [Google Scholar] [CrossRef]
  18. Bai, K.X.; Chang, N.B.; Gao, W. Quantification of relative contribution of Antarctic ozone depletion to increased austral extratropical precipitation during 1979–2013. J. Geophys. Res. Atmos. 2016, 121, 1459–1474. [Google Scholar] [CrossRef]
  19. Krzyścin, J.W.; Baranowski, D.B. Signs of the ozone recovery based on multi sensor reanalysis of total ozone for the period 1979–2017. Atmos. Environ. 2019, 199, 334–344. [Google Scholar] [CrossRef]
  20. Weber, M.; Arosio, C.; Coldewey-Egbers, M.; Fioletov, V.E.; Frith, S.M.; Wild, J.D.; Tourpali, K.; Burrows, J.P.; Loyola, D. Global total ozone recovery trends attributed to ozone-depleting substance (ODS) changes derived from five merged ozone datasets. Atmos. Chem. Phys. 2022, 22, 6843–6859. [Google Scholar] [CrossRef]
  21. Chen, A.; Chen, D.; Hu, X.Y.; Harth, C.M.; Young, D.; Mühle, J.; Krummel, P.B.; O’Doherty, S.; Weiss, R.F.; Prinn, R.G.; et al. Historical trend of ozone-depleting substances and hydrofluorocarbon concentrations during 2004–2020 derived from satellite observations and estimates for global emissions. Environ. Pollut. 2023, 316, 120570. [Google Scholar] [CrossRef] [PubMed]
  22. Mears, C.A.; Wentz, F.J. Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records from the MSU and AMSU Microwave Sounders. J. Atmos. Ocean. Technol. 2009, 26, 1040–1056. [Google Scholar] [CrossRef]
  23. Tritscher, I.; Pitts, M.C.; Poole, L.R.; Alexander, S.P.; Cairo, F.; Chipperfield, M.P.; Grooß, J.-U.; Höpfner, M.; Lambert, A.; Luo, B.; et al. Polar stratospheric clouds: Satellite observations, processes, and role in ozone depletion. Rev. Geophys. 2021, 59, e2020RG000702. [Google Scholar] [CrossRef]
  24. Kessenich, H.E.; Seppälä, A.; Rodger, C.J. Potential drivers of the recent large Antarctic ozone holes. Nat. Commun. 2023, 14, 7259. [Google Scholar] [CrossRef]
  25. Keeble, J.; Hassler, B.; Banerjee, A.; Checa-Garcia, R.; Chiodo, G.; Davis, S.; Eyring, V.; Griffiths, P.T.; Morgenstern, O.; Nowack, P.; et al. Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100. Atmos. Chem. Phys. 2021, 21, 5015–5061. [Google Scholar] [CrossRef]
  26. Bengtsson, L.; Hagemann, S.; Hodges, K.I. Can climate trends be calculated from reanalysis data. J. Geophys. Res. Atmos. 2004, 109, 112–123. [Google Scholar] [CrossRef]
  27. Gualtieri, G. Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review. Renew. Sustain. Energy Rev. 2022, 167, 112741. [Google Scholar] [CrossRef]
  28. Gai, Y.Y.; Long, X.J.; Wei, Z.H.; Sui, X.; Jiang, B.; Chen, X.; Yu, D.; Qin, S.; Cui, Y. Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data. Atmos. Res. 2023, 288, 106742. [Google Scholar] [CrossRef]
  29. Wargan, K.G.; Labow, S.; Frith, S.; Pawson, S.; Livesey, N.; Partyka, G. Evaluation of the Ozone Fields in NASA’s MERRA-2 Reanalysis. J. Clim. 2017, 30, 2961–2988. [Google Scholar] [CrossRef]
  30. Sepúlveda, E.; Cordero, R.R.; Damiani, A.; Feron, S.; Pizarro, J.; Zamorano, F.; Kivi, R.; Sánchez, R.; Yela, M.; Jumelet, J.; et al. Evaluation of Antarctic Ozone Profiles derived from OMPS-LP by using Balloon-borne Ozonesondes. Sci. Rep. 2021, 11, 4288. [Google Scholar] [CrossRef]
  31. Wang, H.; Wang, Y.; Cai, K.; Zhu, S.; Zhang, X.; Chen, L. Evaluating the Performance of Ozone Products Derived from CrIS/NOAA20, AIRS/Aqua and ERA5 Reanalysis in the Polar Regions in 2020 Using Ground-Based Observations. Remote Sens. 2021, 13, 4375. [Google Scholar] [CrossRef]
  32. World Meteorological Organization (WMO). Scientific Assessment of Ozone Depletion: 2022; GAW Report No. 278; WMO: Geneva, Switzerland, 2022; 509p. [Google Scholar]
  33. Zhang, Y.; Li, J.; Zhou, L.B. The relationship between polar vortex and ozone depletion in the Antarctic stratosphere during the period 1979–2016. Adv. Meteorol. 2017, 2017, 3078079. [Google Scholar] [CrossRef]
  34. Proffitt, M.H.; Margitan, J.J.; Kelly, K.K.; Loewenstein, M.; Podolske, J.R.; Chan, K.R. Ozone loss in the Arctic polar vortex inferred from high-altitude aircraft measurements. Nature 1990, 347, 6288. [Google Scholar] [CrossRef]
  35. Anderson, J.G.; Toohey, D.W.; Brune, W.H. Free radicals within the Antarctic vortex: The role of CFCs in Antarctic ozone loss. Science 1991, 251, 39–46. [Google Scholar] [CrossRef]
  36. Hassler, B.; Bodeker, G.E.; Solomon, S.; Young, P.J. Changes in the polar vortex: Effects on Antarctic total ozone observations at various stations. Geophys. Res. Lett. 2011, 38, L01805. [Google Scholar] [CrossRef]
  37. Grytsai, A.V.; Evtushevsky, O.M.; Agapitov, O.V.; Klekociuk, A.R.; Milinevsky, G.P. Structure and long-term change in the zonal asymmetry in Antarctic total ozone during spring. Ann. Geophys. 2007, 25, 361–374. [Google Scholar] [CrossRef]
  38. Engel, A.; Bönisch, H.; Ostermöller, J.; Chipperfield, M.P.; Dhomse, S.; Jöckel, P. A refined method for calculating equivalent effective stratospheric chlorine. Atmos. Chem. Phys. 2018, 18, 601–619. [Google Scholar] [CrossRef]
  39. Lin-Gen, B.; Zhong, L.; Xiang-Dong, Z.; Yong-Feng, M.; Long-Hua, L. Trend of Antarctic ozone hole and its influencing factors. Adv. Clim. Change Res. 2012, 3, 68–75. [Google Scholar] [CrossRef]
  40. Solomon, S.; Ivy, D.; Kinnison, D.; Mills, M.; Neely, R.; Schmidt, A. Emergence of healing in the Antarctic ozone layer. Science 2016, 353, 269–274. [Google Scholar] [CrossRef]
  41. van der A, R.J.; Allaart, M.A.F.; Eskes, H.J. Multi sensor reanalysis of total ozone. Atmos. Chem. Phys. 2010, 10, 11277–11294. [Google Scholar] [CrossRef]
  42. van der A, R.J.; Allaart, M.A.F.; Eskes, H.J. Extended and refined multi sensor reanalysis of total ozone for the period 1970–2012. Atmos. Meas. Tech. 2015, 8, 3021–3035. [Google Scholar] [CrossRef]
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Figure 1. Locations of Antarctic stations. Solid markers indicate Antarctic station locations; colors match their names and abbreviations.
Figure 1. Locations of Antarctic stations. Solid markers indicate Antarctic station locations; colors match their names and abbreviations.
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Figure 2. Correlation coefficient R (unit: dimensionless) assessment results: (a) September; (b) October; (c) November; (d) Spring Average. Solid markers represent 2σ significance; open markers indicate non-significant correlations.
Figure 2. Correlation coefficient R (unit: dimensionless) assessment results: (a) September; (b) October; (c) November; (d) Spring Average. Solid markers represent 2σ significance; open markers indicate non-significant correlations.
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Figure 3. Root-mean-square error RMSE (unit: DU) assessment results: (a) September; (b) October; (c) November; (d) Spring Average.
Figure 3. Root-mean-square error RMSE (unit: DU) assessment results: (a) September; (b) October; (c) November; (d) Spring Average.
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Figure 4. Interannual variability skill score (IVS) (unit: dimensionless) assessment results: (a) September; (b) October; (c) November; (d) Spring Average.
Figure 4. Interannual variability skill score (IVS) (unit: dimensionless) assessment results: (a) September; (b) October; (c) November; (d) Spring Average.
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Figure 5. Average values of evaluation metrics (R, RMSE, and IVS) at 8 stations for each dataset: (a) September; (b) October; (c) November; (d) Spring Average.
Figure 5. Average values of evaluation metrics (R, RMSE, and IVS) at 8 stations for each dataset: (a) September; (b) October; (c) November; (d) Spring Average.
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Figure 6. Absolute TrBias (unit: DU/dec) during the Antarctic ozone recovery period, 2000–2019: (a) September; (b) October; (c) November; (d) Spring Average. Solid markers represent 1σ significance; open markers indicate non-significant biases.
Figure 6. Absolute TrBias (unit: DU/dec) during the Antarctic ozone recovery period, 2000–2019: (a) September; (b) October; (c) November; (d) Spring Average. Solid markers represent 1σ significance; open markers indicate non-significant biases.
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Figure 7. Average interdecadal TrBias (unit: DU/dec) of each dataset at 8 Antarctic stations during the ozone recovery period, 2000–2019: (a) September; (b) October; (c) November; (d) Spring Average. The red dashed lines indicate the 1σ confidence interval.
Figure 7. Average interdecadal TrBias (unit: DU/dec) of each dataset at 8 Antarctic stations during the ozone recovery period, 2000–2019: (a) September; (b) October; (c) November; (d) Spring Average. The red dashed lines indicate the 1σ confidence interval.
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Table 1. Data time periods, locations, and observation instruments of 8 investigated stations.
Table 1. Data time periods, locations, and observation instruments of 8 investigated stations.
Station Name (Abbreviation)Time PeriodLocationInstrument
Arrival Heights (AH)1988–2019166.67° E, 77.83° SDobson
Dumont D’Urville (DD)1988–2019140.02° E, 66.66° SSAOZ
Faraday/Vernadsky (FV)1980–201964.25° W, 65.24° SDobson
Halley (H)1980–201925.21° W, 75.57° SDobson
Marambio (M)1987–201956.62° W, 64.23° SBrewer
Rothera (R)1996–201967.56° W, 68.13° SSAOZ
Syowa (S)1980–201939.58° E, 69.00° SBrewer
ZhongShan (ZS)1993–201976.38° E, 69.37° SBrewer
Table 2. Details of reanalysis and merged satellite datasets.
Table 2. Details of reanalysis and merged satellite datasets.
DataResolution (Lon° × Lat°)CategoryTime PeriodSource
ERA50.25 × 0.25Reanalysis1940–2023ECMWF
NIWA-BS1.25 × 1.0Merged Satellite1979–2019NIWA
C3S-MSR0.5 × 0.5Reanalysis1979–2022C3S
JRA-551.25 × 1.25Reanalysis1958–2023JMA
MERRA-20.625 × 0.5Reanalysis1980–2023NASA
Table 3. Statistical metrics for evaluation in the study.
Table 3. Statistical metrics for evaluation in the study.
MetricDescriptionOptimal ValueUnit
RPearson correlation coefficient1Dimensionless
RMSERoot-mean-square error0DU
IVSInterannual variability skill score0Dimensionless
TrBiasTrend bias0DU/year
Table 4. Average ranking of the three metrics for different datasets.
Table 4. Average ranking of the three metrics for different datasets.
PeriodC3S-MSRERA5NIWA-BSMERRA-2JRA-55
Sep12.332.664.674.33
Oct12.663.3335
Nov1.332.334.332.334.67
SON12.332.6745
Average1.082.423.253.54.75
Table 5. Average ranking of trend for different datasets.
Table 5. Average ranking of trend for different datasets.
PeriodC3S-MSRNIWA-BSMERRA-2JRA-55ERA5
Sep21534
Oct24135
Nov21354
SON12354
Average1.752344.25
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Chen, J.; Zhang, Y.; Shi, H.; Hu, H.; Xu, J. Applicability Evaluation of Antarctic Ozone Reanalysis and Merged Satellite Datasets. Atmosphere 2025, 16, 696. https://doi.org/10.3390/atmos16060696

AMA Style

Chen J, Zhang Y, Shi H, Hu H, Xu J. Applicability Evaluation of Antarctic Ozone Reanalysis and Merged Satellite Datasets. Atmosphere. 2025; 16(6):696. https://doi.org/10.3390/atmos16060696

Chicago/Turabian Style

Chen, Junzhe, Yu Zhang, Houxiang Shi, Hao Hu, and Jianjun Xu. 2025. "Applicability Evaluation of Antarctic Ozone Reanalysis and Merged Satellite Datasets" Atmosphere 16, no. 6: 696. https://doi.org/10.3390/atmos16060696

APA Style

Chen, J., Zhang, Y., Shi, H., Hu, H., & Xu, J. (2025). Applicability Evaluation of Antarctic Ozone Reanalysis and Merged Satellite Datasets. Atmosphere, 16(6), 696. https://doi.org/10.3390/atmos16060696

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